P(doom) estimates from AI researchers span approximately six orders of magnitude — from Yann LeCun's under 1% to Eliezer Yudkowsky's over 95%. This enormous range reflects genuine, substantive disagreement about the future of AI and the difficulty of the alignment problem. Understanding why experts disagree is essential for anyone trying to form their own view of AI existential risk.
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P(doom) Calculator →Expert Estimates Overview
Here are prominent public estimates from leading AI researchers and industry figures:
The AI Impacts Expert Survey (ESPAI)
The most systematic data comes from AI Impacts' Expert Survey on Progress in AI. The latest iteration — ESPAI 2024, fielded December 2024 across 1,580 international AI researchers and published September 2026 — found:
- Median P(extinction or permanent severe disempowerment): 10% — double the 5% median of the previous survey
- Mean: 18%
- 51% of researchers assigned at least a 10% probability
- 72% believe AI-risk-minimization research should be prioritised more
This survey is often cited as the closest thing to an "expert consensus" — but the spread between median and mean, and the doubling of the median between iterations, shows that expert views remain unsettled and have moved upward.
Why Experts Disagree
The enormous spread in estimates is not random — it reflects systematic disagreement about specific questions:
1. AGI Timelines
When will transformative AI arrive? Optimists (often with lower P(doom)) expect longer timelines (50+ years), giving more time to solve alignment. Pessimists expect shorter timelines (5-15 years), leaving insufficient time for safety work. The ESPAI 2024 survey found the median forecast for high-level machine intelligence had moved forward to ~2047 — and individual estimates still ranged from never to within a decade.
Why P(doom) Went Mainstream in 2025–2026
Two developments pushed P(doom) from an insider metric into public discussion. First, Eliezer Yudkowsky and Nate Soares' 2025 book If Anyone Builds It, Everyone Dies — a New York Times bestseller — gave the 95%+ worldview mass-market exposure. Second, September 2026 brought the topic to front pages: researcher Jacob Coxon resigned accusing frontier labs of "playing with our lives," Anthropic's head of alignment Evan Hubinger publicly gave his estimate as ">10% within the next decade," and NVIDIA CEO Jensen Huang countered with "zero." In Silicon Valley, stating one's personal P(doom) at dinners and on panels has become a shorthand for where someone stands on AI risk — much like stating one's position on crypto or longevity once was.
2. Alignment Difficulty
Is alignment a solvable engineering problem (LeCun's view) or a fundamentally hard theoretical problem (Yudkowsky's view)? This is perhaps the core disagreement. Those who view alignment as tractable tend to have lower P(doom); those who view it as extremely difficult tend to have higher P(doom). Recent progress in RLHF and Constitutional AI has moved some estimates downward, but skeptics argue these methods won't scale to superintelligence.
3. Capability Jump
Will AI capabilities increase gradually (allowing course correction) or suddenly (removing time to respond)? Gradualists argue we'll have warning signs and time to adapt. Sudden-jump theorists worry about "intelligence explosion" scenarios where AI self-improvement rapidly outpaces human response.
4. Societal Response
Will international coordination succeed? Will competitive pressure override safety investment? Optimists about governance tend to have lower P(doom); pessimists about race dynamics tend to have higher P(doom). The current competitive landscape between major AI labs and nations is a significant concern for many researchers.
5. Definition of "Doom"
Experts use different definitions. Some define doom as literal human extinction; others include civilisational collapse, permanent loss of autonomy, or "any outcome significantly worse than a good future." Broader definitions naturally produce higher estimates.
Calculate Your Own P(doom)
Our P(doom) Calculator lets you explore how changing your assumptions about these key variables affects the combined probability estimate. It's a useful tool for stress-testing your intuitions about AI risk and understanding how different experts might arrive at their estimates.
P(doom) Calculator
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